English

If sufficient demand

The goal of the course is
to give students the tools and training to recognize convex optimization problems that arise in applications

to present the basic theory of such problems, concentrating on results that are useful in computation

to give students a thorough understanding of how such problems are solved, and some experience in solving them

to give students the background required to use the methods in their own research work or applications

The course has three parts
* Basic theory for convex sets and functions
* Experience of formulation of application problems as convex optimization problems
* Knowledge and experience of efficient optimization algorithms

* have knowledge about theory for convex sets and functions
* understand how application problems can be formulated as convex optimization problems
* have demonstrated understanding of how efficient algorithms are implemented and works

* have demonstrated skills in calculations with convex functions
* be able to reformulate practical application problems as convex optimization problems
* demonstrated ability in handling some existing program package for convex optimization and ability in writing code for simpler algorithms

demonstrate the ability to critically evaluate and compare different formulations of convex optimization problems and different algorithms for different quality criteria

Lectures

Exercises

Written exam

Written assignments

Miscellaneous

Weekly handin problems
Take-home exam
Students should take an active role in the weekly exercise sessions

Failed, pass

Linera algebra, calculus in several variables, probability theory

Boyd, S. & Vandenberghe, L.: Convex Optimization. Cambridge University Press.

Freely available on http://www.stanford.edu/~boyd/cvxbook/

Replaces FRT015F.

FRT016F

 -03-03

FN1/Anders Gustafsson